Traditional materials development focuses on the forward problem of relating materials structure to properties. As such, this approach seeks answers to the question: 'given a set of material attributes (composition, microstructure), what are the corresponding properties and performance characteristics?' On the other hand, materials-by-design reverses this relationship. The problem is then one of synthesis, as the question is: 'given desired performance attributes, what is the necessary material composition and the required synthesis/processing routes?'

In general, inverse problems cannot be solved rigorously and solutions are at best approximate. In the context of materials design, the problem is even more challenging since formal optimization/design require explicit mathematical models for structure-properties relations. While the realistic simulation of materials, considering all the relevant scales, remains an unsolved problem, the real goal of the materials design framework is to come up with possible viable solutions in which perfect fidelity with respect to the performance of the actual materials systems is not as important as long as the predicted performance using simplified models provides simulated solutions that have similar (ideally the same) rank order as the actual experiments. In colloquial terms, in materials design, models do not have to be tremendously accurate as long as they are good enough.

In my group, we have been applying optimization schemes to design advanced multi-component, multi-phase materials, with particular emphasis on Transformation-Induced Plasticity (TRIP)-assisted steels. TRIP-assisted steels are good candidates for the next generation high strength automotive structural materials due to their high strength and relatively high ductility enhanced through strain-induced martensitic transformation of retained austenite. TRIP steels consist of multi-phase microstructures (ferrite, bainite, martensite and retained austenite), whose properties are greatly affected by their phase constitution, which in turn can be controlled through complex heat treatment schedules.

In this talk, I first present recently developed thermodynamic and kinetic models to predict the phase constitution of low-alloy TRIP steels as a function of heat treatment parameters. The models consider the partitionless nature of the nucleation of bainite, the heterogeneous distribution of carbon as it is rejected from the bainitic ferrite into the remaining austenite and the stabilization of retained austenite against martensitic transformation due to carbon enrichment. The models are validated through detailed experimental work. The mechanical properties of the resulting multi-phase microstructure are investigated through strain hardening models taking into account the irreversible thermodynamics of dislocation generation and Mecking-Kocks-type models. The models (fitted to experiments) account for the strain-induced transformation of austenite.

The models for phase constitution and mechanical performance are then incorporated into a Genetic Algorithm optimization tool that optimizes strength and ductility of the microstructure as a function of heat treatment temperatures and alloy compositions. The model is tested against a low alloy TRIP steel with nominal composition Fe-0.32C-1.42Mn-1.56Si and is then applied to the development of a new class of TRIP steels with Fe-C-Mn-Si-Al-Cu as their major constituents.

Dr. Arroyave obtained his BS degrees in Mechanical and Electrical Engineering from the Instituto Tecnologico y de Estudios Superiores de Monterrey (Mexico) in 1996. He got his MS in Materials Science and Engineering in 2000 and his PhD in Materials Science in 2004 from MIT. After a postdoc at Penn State, he joined the Department of Mechanical Engineering at Texas A&M University in 2006. Since August 2012, he is a faculty member of the newly created Department of Materials Science and Engineering at Texas A&M University.

Dr. Arroyave's area of expertise is in the field of computational materials science, with emphasis in computational thermodynamics and kinetics of materials. He and his group use different techniques across multiple scales to predict and understand the behavior of inorganic materials (metallic alloys and ceramics). The techniques range from ab initio methods, classical molecular dynamics, computational thermodynamics as well as phase-field simulations.

Over the past seven years, Dr. Arroyave and his group have been using these techniques to investigate a wide range of materials, such as high-temperature shape memory alloys, ferromagnetic shape memory alloys, hydrogen storage materials, materials for electric interconnects in microelectronic packaging, novel steel alloys as well as nuclear fuels for next-generation nuclear power plants.

More recently, Dr. Arroyave has been collaborating with colleagues in the fields of micrsostructural design and design theory to develop inverse methods for the discovery and design of multi-component, multi-phase structural materials, with special emphasis on Transformation Induced Plasticity (TRIP) Steels.

Dr. Arroyave has been co-author of 70 publications in peer-reviewed journals, 13 conference proceedings as well as close to 80 conference papers and 35 invited talks in the US and abroad. In 2012 he was awarded the TEES Select Young Faculty Fellow Award by the College of Engineering at Texas A&M University. He also received Honorable Mention as an Early Career Faculty Fellow of TMS. In 2010 he was awarded the CAREER Award from NSF. Earlier (2006), he was awarded the Young Leader Professional Development Award from TMS.

He currently serves as the Chair of the Alloy Phases Committee at TMS, as Secretary of ASM's Alloy Phase Diagram Committee and as active member in the ICME, Physics and Chemistry of Materials, Computational Materials Science and Award Committees at TMS. He has also chaired and co-chaired multiple symposia at TMS and MS&T. He is the 2014 recipient of the TMS-EMPMD Distinguished Service Award.